Machine learning in eCommerce can help reduce cart abandonment, increase time spent on the website, maximize conversion rates and serve plenty of other benefits.

Sounds too optimistic? Think about this. eCommerce stores are basically online shopping malls. They produce tons of data on a real-time basis that can give straightforward hints about customer preferences and behavior.

Machine Learning algorithms can analyze and make meaningful conclusions from massive amounts of data that humans, even with supercomputers cannot sit through. In the eCommerce industry, machine learning can help fill improve the customer experience at several junctures.

The opportunity is so promising that retail giants like Amazon and Walmart are leaning on machine learning to enhance omnichannel customer shopping experiences.

A machine learning system can replace an entire human-powered process, thus, removing the possibilities of errors, predicting anomalies and accelerating decision-making; all at fractional costs.

Here are some ways how Machine Learning combined with Artificial Intelligence will help eCommerce stores like Amazon, Macy’s, eBay, Flipkart and the sorts maximize their sales volumes.

Autonomous Chatbots

We live in the era of conversational commerce. Customers are increasingly communicating with brands through instant messaging apps and social media platforms before buying a product. Even a large chunk of after-sales service begins with a conversation.

Chatbots powered with Machine Learning and Natural Language Understanding (NLU) capabilities can engage in real-time conversations with customers. They can help customers pick the right product that best suits customer preferences.

eBay’s ShopBot is a classic example of chatbots in eCommerce.

Autonomous chatbots can deliver a personal shopping experience to customers by giving personal attention. They become personal shopping assistants who continuously learn from customer inputs and deliver accurate responses that are closely aligned with the customer’s historical interests and requests.

As they say in the business world, “A well-attended customer is one who returns for more shopping, which translates into a bigger customer base and larger sales volumes.”

Dynamic Product Suggestions

Upselling and Cross-selling are two strategies that almost every eCommerce store deploys to maximize their revenue. But, they cannot be easily implemented. Manual picking of products for can cost expensive man hours and is literally impossible. And, there is also the inherent risk that the manual suggestions may not be best fit for the customer at all.

Machine Learning can pitch in here with its swift data analytic abilities and zero in on products that customers might be interested to buy. They can give dynamic product suggestions that help customers ‘complete the look’ or bundle together products for convenient use. Machine Learning helps offer multiple bundle suggestions to customers, thus persuading them to buy more than what they had initially planned for.

This ultimately swells the sales volume for the eCommerce business without any additional cost or manpower requirements.

Market-driven anchor pricing

Online retailers have to rely on extensive anchor pricing strategies to drive sales. They have to slash margins and fluctuate prices in a moment’s notice to stay competitive and to retain their customers.

There are also holiday season sales which makes it extra difficult to fix market-driven prices. Often, when there is no other go, eCommerce store owners resort to anchor pricing based on a guesswork. Sometimes it works, most often, it fails.

Machine Learning can take away the guesswork in anchor pricing by giving accurate inputs that are aligned with market trends. The ML system gives adequate weightage based on preset parameters, like which competitor pricing should be given more weightage, what products should be prioritized, etc.

It gathers, analyses and throws out data that helps retailers fix prices with a competitive advantage. To deliver accurate results in this model, Machine Learning systems follow the IFTTT (If This Then That) principle which works as below:

Airlines, online travel operators, online hotel booking websites were the first to embrace Machine Learning for perfecting their pricing strategies.

Real-time Data Analytics

Data is like fuel for eCommerce industry. It helps create the right product mix, pricing and allied services that will maximize conversions. While analytic tools like Big Data are gaining momentum, there is still a gaping hole that the eCommerce industry is staring at.

It is still difficult to put finger on the right data and derive meaningful patterns from it. It is here that Machine Learning pitches in. it helps segregate, sort, group, cluster and analyze data in various forms which simplifies decision-making. Some metrics also aid in the long-term planning of the business thus helping strengthen profitability from the grassroots level.

Here is some real-time data analytics measure that Machine Learning can provide:

  • Customer Segmentation
  • Churn prediction
  • Cart abandonment reasons
  • Sentiment analysis
  • Inventory management & forecasting
  • Anticipatory shipping and planning

Bringing It All Together

Machine Learning will make computers intelligent and powerful than they are today. Computer systems will evolve through continuous learning based on constant flow of data from humans. At some time in the future, as most scientific visionaries like Stephen Hawking and Elon Musk opine, machines will have an upper hand on humans.

eCommerce will gain significantly from Machine Learning. It will help predict customer behavior, understand real-time data and take right decisions at the right time that will lead to more sales volumes.